sm_regression: Kernel-weighted regression models
In ddsjoberg/sjosmooth: Kernel smoothing for time to event data

Description Usage Arguments Examples

The user specifies a regression model and a variable for weighting, and 'sm_regression“ will estimate a weighted regression model for each unique value of the specified variable.

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sm_regression(data, method, formula, weighting_var, newdata = data,
  method.args = NULL, lambda = 1, kernel = "epanechnikov",
  dist.method = "euclidean", verbose = FALSE)

`data`	data frame
`method`	function to use
`formula`	formula
`weighting_var`	columns name(s) of variables used to calculate weights
`newdata`	new data frame. Default is 'data'.
`method.args`	List of additional arguments passed on to the modelling function defined by 'method'
`lambda`	The radius of the kernel for tri-cubic, Epanechnikov, and flat kernels. The standard deviation for the Gaussian kernel
`kernel`	Specifies the kernel to be used: 'epanechnikov', 'tricube', 'gaussian', and 'flat' are accepted. Default is 'epanechnikov'
`dist.method`	Specifies the distance measure to be used in the kernel. Default is 'euclidean'. Distance measures accepted by `stats::dist` is acceptable.
`verbose`	Return full set of results as an attribute. Default is 'FALSE'

sm_regression(
  data = mtcars,
  method = "glm",
  formula = am ~ mpg,
  weighting_var = "mpg",
  method.args = list(family = binomial(link = "logit"))
)